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OPEN Glomalin contributed more to carbon, nutrients in deeper , and diferently associated with Received: 20 July 2017 Accepted: 18 September 2017 climates and properties in Published: xx xx xxxx vertical profles Wenjie Wang1,2, Zhaoliang Zhong1, Qiong Wang1,2, Humei Wang1, Yujie Fu1 & Xingyuan He2

Despite vital importance in soil conditioning and a proxy for arbuscular mycorrhizal (AMF), glomalin- related soil protein (GRSP) contribution to soil carbon and nutrients at vertical soil profles and underlying mechanism were not well-defned yet. Thus, 360 soil samples were collected from 72 farmland 1-m soil profles in northeastern China, and soil physiochemical properties, nutrients, glomalin characteristics, local climates were determined. Linear decreases of glomalin amounts were observed from the top to deep soils, and glomalin/SOC (glomalin ratio to total SOC) in the 80–100 cm soil (EEG, easily-extracted GRSP, 2.2%; TG, total GRSP, 19%) was 1.34–1.5-fold higher than did in the 0–20 cm soil. Diferent statistical analyses crosschecked that the lower pH and higher SOC usually accompanied with the higher EEG and TG, while EEG was more sensitive to climates; Moreover, glomalin was more physiochemical-regulated in the deep soils, but more nutrient-regulation was found in the surface soils. Structure Equation Model showed that soil depths and climates indirectly afected TG and EEG features through soil properties, except signifcant direct efects on EEG. In future, glomalin assessment should fully consider these for identifying the AMF importance in the whole 1-m profle, and our fndings also favor degrade soil improvement from glomalin rehabilitation.

Glomalin-related soil protein (GRSP) is a glycoprotein produced by the hyphae of arbuscular mycorrhizal fungi (AMF) that contains large contents of metal ions1,2. GRSP amount received increasing attention over the past few years for the anti-erosion ability of GRSP in soil aggregate formation3–5 and the physical networking function of AM hyphae6, loading capacity for heavy metals2, maintaining soil fertility7, increasing soil C capture8–11 and enhancing the availability of polycyclic aromatic hydrocarbons in soil12. Many studies proved that GRSP has a complex composition of asparagine-linked carbohydrate chains13,14 and it is a mixture of organic matter, amino acids and carbohydrates along with some aliphatic methines, methylenes3 and possibly some auto-fuorescent compounds tightly bound with metal ions15,16. However, limited information is available regarding glomalin var- iation at vertical soil profles and underlying mechanism related soil properties and climatic variations, and this information will beneft the local degraded soil improvements and exact evaluations of AMF-importance14,16,17. Shallow-soil-sampling is ofen justifed by assuming that deeper soil horizons are stable and will not change over time, although some medium- and long-term studies do not support this assumption18. In a shallow 40 cm soil profle, soil glomalin exhibits obvious vertical decreasing pattern19, and supply of fresh plant-derived carbon to the subsoil (0.6–0.8 m depth) stimulated the microbial mineralization of 2,567 plus minus 226-year-old car- bon20. Te fraction of soil organic carbon (SOC) that may be attributed from glomalin, glomalin/SOC, may be taken as a probe of either enhanced stability or preferential production of glomalin21. Given that glomalin/SOC or glomalin/nutrients ratio is higher in the deep soils, glomalin became more important in the deep soils than did in the surface soils, owing to their possible higher contribution to soil C sequestration and nutrient storage; And the defnition of this hypothesis needs a soil sampling at the deep soil profles. Te total AMF infection level and

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Scientific RepoRts | 7: 13003 | DOI:10.1038/s41598-017-12731-7 1 www.nature.com/scientificreports/

Depth cm EEG TG EEG/SOC TG/SOC EEG/N TG/N EEG/P TG/P EEG/TG Average of pooled values 0–20 0.745d 5.80d 0.017a 0.13a 0.022a 0.16ab 0.013a 0.090a 0.14a 20–40 0.563c 4.17c 0.019a 0.13a 0.019a 0.14a 0.010a 0.071a 0.16a 40–60 0.413b 3.50bc 0.018a 0.15ab 0.018a 0.14a 0.013a 0.13ab 0.20a 60–80 0.328ab 3.07ab 0.020a 0.17bc 0.023a 0.19b 0.010a 0.10ab 0.18a 80–100 0.290a 2.40a 0.022a 0.19c 0.024a 0.19b 0.043b 0.25b 0.21a y = −0.006x y = −0.040x y = 7E- y = 0.001x y = 5E-05x y = 0.001x y = 0.0003x y = 0.002x y = 0.001x Linear changes + 0.75 + 5.8 05 + 0.02 + 0.11 + 0.02 + 0.14 + 0.003 + 0.04 + 0.140 R2 0.230 0.228 0.02 0.06 0.01 0.03 0.013 0.012 0.009 p-level 0.000 0.000 0.011 0.000 0.131 0.003 0.032 0.037 >0.05 CV% (coefcient of variation) 0–20 0.36 0.37 0.42 0.35 0.51 0.41 2.27 1.92 0.40 20–40 0.53 0.51 0.55 0.46 0.66 0.62 0.77 0.81 0.59 40–60 0.80 0.61 0.67 0.53 0.83 0.51 1.36 2.01 1.35 60–80 0.94 0.68 1.00 0.56 0.97 0.64 1.47 1.42 1.47 80–100 0.98 0.73 0.85 0.69 0.86 0.70 3.77 3.74 1.43

Table 1. Diferences in EEG and TG amounts at diferent soil depths. Diferent lowercases in the same column at each group indicates the diferences between soil depths, statistically signifcant at p < 0.05. Note: EEG/ SOC, relative ratio of the carbon in EEG to total SOC; TG/SOC, relative ratio of the carbon in TG to total SOC; EEG/N, relative ratio of the nitrogen in EEG to total soil N; TG/N, relative ratio of the nitrogen in TG to total soil N; EEG/P, relative ratio of the phosphorus in EEG to total soil P; TG/P, relative ratio of the phosphorus in TG to total soil P; EEG/TG, relative ratio of the EEG to TG.

glomalin (easily-extracted GRSP, EEG; and total GRSP, TG) were positively correlated with soil edaphic factors, indicating that AM fungal infections and glomalin may be useful to monitor desertifcation and degraded soil improvement22. Within the top 100 cm of farmland soil, AMF declined with increasing soil depth, however, AMF diversity was not diferent by soil depth23. As a proxy of AMF biomass, the vertical glomalin distribution in a deep soil profle and possible associations with soil properties and climates at sampling sites did not fully be analyzed to date. Tus, a clear understanding of diferences between the surface and deep soils in glomalin characteristics and possible underlying mechanism, e.g., associations with soil properties and sampling site climatic conditions, is essential for the scientifc assessments of glomalin and AMF importance for farmland productivity in NE China. Technological advances have enabled a new class of multivariate models for ecology, with the potential now to specify a statistical model for abundances jointly across many taxa, to simultaneously explore interactions across taxa and the response of abundance to environmental variables24. Structural equation modeling (SEM) is a powerful, multivariate technique found increasingly in scientifc investigations to test and evaluate multivariate causal relationships25, and powerful in from patterns to causalunderstanding26. Pearson correlation and stepwise regression are common-used methods for fnding complex associations and possible casual rela- tions27–30. Canonical ordinations are invaluable tools for modeling communities through environmental predic- tors, and variation partitioning can then be used to test and determine the likelihood of these sets of predictors in explaining patterns in community structure31, is also powerful in identifying co-occurring species on transloca- tion success in Iris atrofusca32. Ecologists and evolutionary biologists rely on an increasingly sophisticated set of statistical tools to describe complex natural systems33, and a large feld dataset together with multiple statistical tools is possibly cross-checking the reliability of the results and also underlying mechanism clarifcations34,35. In this paper, we hypothesized that both GRSP amounts and their contribution to SOC and nutrients were soil depth-dependent, and these glomalin features could be strongly regulated by soil properties and climatic diferences. Tree questions will be answered in this paper, i.e., 1) What’re the diferences of glomalin at a 1-m soil profle [including EEG, TG, EEG/TG and their contribution to SOC and nutrients]? 2) What’s the diferences of various soil nutrients (SOC, nitrogen [N], available N [AN], phosphorus [P], available P [AP], potassium [K] and available K [AK]) and soil physiochemical properties (pH, electrical conductivity [EC] and soil water) at a 1-m soil profle? 3) which factors of soil nutrients, physiochemical properties and climatic conditions at sampling sites are responsible for the glomalin variations, and how large diferences existed in the deep and surface soils? Te answers to abovementioned questions may support possible scientifc evaluation of glomalin importance in soil improvement and possible measures for degraded soil rehabilitation from the viewpoint of glomalin recovery. Results Glomalin vertical variations: average and deviation. Two components of glomalin (EEG and TG) lin- early decreased from the top soil to the deep soil (p < 0.001), and peak values in the surface soil were 0.742 g kg−1 for EEG, 6.041 g kg−1 for TG (Table 1). Teir ratio to total SOC linearly increased with soil depth, showing the much higher glomalin contribution to SOC in the deep soils. For example, in the deep 40–100 cm soils, 1.11–1.31 fold higher glomalin (EEG and TG)/SOC ratios were found compared with those in the surface 0–40 cm soils. Similarly, EEG/N, TG/N, EEG/P, TG/P showed linear increases with the soil depth. Te deviations of glomalin at soil profle increasing with the soil depth was generally observed. For example, in the case of Coefcient of Variation (CV = standard error/average), linear increases were found in CVs of EEG, TG, EEG/SOC, TG/SOC,

Scientific RepoRts | 7: 13003 | DOI:10.1038/s41598-017-12731-7 2 www.nature.com/scientificreports/

Bulk density Soil water AK Depth cm (g cm−3) (%) pH EC (µS cm−1) SOC (g kg−1) N (g kg−1) AN (mg kg−1) P (g kg−1) AP (mg kg−1) K (g kg−1) (mg kg−1) 0–20 1.42a 12.52a 7.82a 160.30b 17.46e 1.42d 108.0c 0.47d 8.37b 44.3a 82.67b 20–40 1.45ab 14.34b 7.95a 107.25a 12.7d 1.28c 81.3b 0.36c 6.00a 52.1b 62.97a 40–60 1.46bc 12.69a 8.23b 95.31a 8.86c 0.95b 65.3b 0.28b 5.47a 45.0a 61.3a 60–80 1.50c 11.36a 8.23b 98.23a 7.17b 0.63a 28.4a 0.24ab 5.59a 51.6b 49.3a 80–100 1.50c 11.28a 8.28b 93.77a 5.22a 0.51a 34.1a 0.20a 5.56a 60.2c 53.0a Linear y = 0.001x y = −0.03x y = 0.006x y = −0.71 y = −0.15x y = −0.01x y = −1.0x y = 0.003x y = −0.03x y = 0.162x y = −0.37x changes + 1.4 + 13.9 + 7.8 + 146.2 + 18 + 1.57 + 113.3 + 0.5 + 7.6 + 42.7 + 80.5 R2 0.064 0.03 0.06 0.07 0.48 0.40 0.23 0.240 0.02 0.09 0.04 p-level 0.000 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.006 0.000 0.000

Table 2. Variations in soil fertility properties at diferent soil depths (MANOVA) in average and deviation. a, b, c, d, e indicates the diferences between depths, statistically signifcant at p < 0.05.

EEG/N as well as EEG/TG (Table 1). In the case of data deviations (SE), signifcant increases were found in EEG/ SOC, TG/SOC, EEG/N, TG/N, while the linear changes were not statistically signifcant in EEG/P, TG/ P (data not shown here).

Soil property vertical variations. Pooled data regression analysis showed that soil physiochemical prop- erties and soil nutrients showed linearly changes at the vertical profle (Table 2). Soil bulk density and pH showed linear increases with the soil depth, while all others (soil water, EC, SOC, N, AN, P, AP, K and AK) showed linear decreases with the soil depth (Table 2). Average of the soil properties showed that the bulk density in the deep soils was 1.06-fold higher than the sur- face soils. Te soil water was peaked at the 20–40 cm (14.34%). Te pH values in the bottom soils were 1.06-fold higher than that in the surface soils. Te peak EC at the surface soils was 1.7-fold higher than that in the deep soils. Te peak SOC at the surface soils was 3.4-fold higher than that in the 80–100 cm soil, while nearly 3-fold dif- ferences between the surface and deep soils were observed in the N, AN and P. About 1.5-fold diferences between the surface and deep soils were observed in the AK and AP (Table 2). Stepwise regressions found that the glomalin amounts in the surface soils and the deep soils were regulated diferently by the soil properties, soil nutrients and climate conditions in sampling sites (Table 3). Both the surface soils and the deep soils showed that the higher SOC, together with the lower values in pH and MAT was in line with the higher EEG amounts in the surface soils, and R2 in the deep soils was 1.3-fold higher than that in the surface soils (Table 3). In the case of TG, both the surface soils and the deep soils showed that the higher SOC accompanied with the higher TG; However, the stepwise model in the surface soils included climatic conditions (MAT and MAP), while these two parameters were not found in the deep soils and as instead, pH entered in the deep soil’s model (Table 3). In the case of glomalin contribution to total SOC (glomalin/SOC), the lower SOC, pH, and MAT accompa- nied with the higher EEG/SOC and TG/SOC; However, the standard coefcient (beta) for the stepwise regres- sions showed that the SOC’s infuences in the surface soils (−0.45 to −0.60) were about 3-fold higher than those in the deep soils (−0.18 to −0.19). Moreover, the pH’s infuences in the deep soils (−0.48 to −0.59) were usually much higher than those in the surface soils (0 to −0.35). Total climatic infuences in the surface soils (MAT’s beta, −0.29 to −0.41) were similar to those in the deep soils (−0.28 in the deep soils) (Table 3). In the case of glomalin contribution to total N (glomalin/N), the stepwise model included parameters of N, pH, MAT, SOC and soil water. In general, the lower N, pH, and MAT usually accompanied with the higher EEG/N in the surface soils and the deep soils, however, the N’s standard coefcients in the surface soils (beta = −0.50) were about 2-fold higher than that in the deep soils (beta = −0.27). For a higher TG/N both in the surface and deep soils, there were accompanying lower values in N, pH but higher SOC; Beta values for N and SOC in the surface soils were much larger than did in the deep soils, while the beta value for pH showed a contrary tendency (Table 3). In the case of glomalin contribution to total P (glomalin/P), the surface soils showed that the lower P but higher SOC accompanied with the larger EEG/P and TG/P; However, in the deep soils, only soil P entered the stepwise model. Standard coefcient values (beta values) showed that the P’s efects in the surface soils were about 2-fold higher than those in the deep soils (Table 3). Both the surface soils and the deep soils showed that the higher SOC and MAT usually accompanied with the lower EEG/TG, and the SOC’s beta values for the surface soils were about 2.5-fold higher than did in the deep soils, while the MAT’s beta value in the surface soils (−0.38) was the same to that in the deep soils (−0.37) (Table 3). In all, the statistics of whole stepwise regressions showed that in the surface soils, glomalin features were more regulated by soil nutrients, as manifested by the 41.4% parameters as soil nutrients into the stepwise models compared with the much lower percentage in the deep soils (36.7% parameters were soil nutrients); However, soil physiochemical properties greatly regulated glomalin features in the deep soils (43.3% parameters as soil physiochemical properties) compared with those at the surface soils (27.6% parameters as soil physiochemical properties) (Table 3). Of the parameters, the SOC and pH were the most frequent parameters related to soil nutrients and soil phys- iochemical properties, while MAT was the most frequent climatic parameter. In the surface soils, SOC entering 8 times and pH entering 4 times were observed in the stepwise models, while in the deep soils, more pH (6 times) but less SOC (6 times) entered the models. Te standard coefcient beta averages for SOC and pH at the surface

Scientific RepoRts | 7: 13003 | DOI:10.1038/s41598-017-12731-7 3 www.nature.com/scientificreports/

Surface soils 0–40 cm Deep soils 40–100 cm Standard Standard Y Parameters Unstandard B Beta Sig R2 Parameters Unstandard B Beta Sig. R2 SOC 0.02 0.41 0.00 0.53 pH −0.26 −0.59 0.00 0.69 MAT −0.18 −0.31 0.00 MAT −0.17 −0.28 0.00 EEG pH −0.27 −0.60 0.00 EC −0.001 −0.17 0.00 Altitude −0.001 −0.29 0.00 Altitude −0.001 −0.26 0.00 water −0.01 −0.17 0.01 SOC 0.026 0.32 0.00 SOC 0.20 0.50 0.00 0.63 SOC 0.32 0.60 0.00 0.62 MAP 0.06 0.60 0.00 pH −1.28 −0.43 0.00 TG water −0.17 −0.36 0.00 water −0.07 −0.17 0.00 MAT −1.46 −0.34 0.00 SOC −0.001 −0.60 0.00 0.48 MAT −0.009 −0.28 0.00 0.53 MAT −0.007 −0.41 0.00 water −0.001 −0.25 0.00 EEG/SOC pH −0.005 −0.35 0.00 pH −0.015 −0.59 0.00 MAP 0.000 −0.33 0.00 SOC −0.001 −0.18 0.00 SOC −0.004 −0.45 0.00 0.24 pH −0.070 −0.48 0.00 0.30 MAP 0.001 0.616 0.00 water −0.007 −0.34 0.00 TG/SOC water −0.004 −0.34 0.00 SOC −0.005 −0.19 0.01 MAT −0.029 −0.29 0.00 N −0.01 −0.50 0.00 0.58 MAT −0.018 −0.48 0.00 0.55 MAT −0.009 −0.38 0.00 EC −7.8E-5 −0.17 0.00 EEG/N pH −0.006 −0.34 0.00 N −0.013 −0.27 0.00 water −0.001 −0.28 0.00 pH −0.011 −0.36 0.00 water −0.001 −0.23 0.00 N −0.120 −0.80 0.00 0.40 pH −0.071 −0.43 0.00 0.38 TG/N SOC 0.007 0.49 0.00 N −0.135 −0.48 0.00 pH −0.022 −0.18 0.01 water −0.006 −0.26 0.00 SOC 0.010 0.34 0.00 K 0.001 0.18 0.00 P −0.050 −0.45 0.00 0.18 P −0.146 −0.24 0.00 0.06 EEG/P SOC 0.001 0.28 0.00 P −0.314 −0.47 0.00 0.21 P −0.972 −0.26 0.00 0.07 TG/P SOC 0.007 0.33 0.00 SOC −0.006 −0.44 0.00 0.31 MAT −0.19 −0.37 0.00 0.17 EEG/TG MAT −0.057 −0.38 0.00 SOC −0.01 −0.17 0.01 climatic entered/total 9/29 = 31.0% 6/30 = 20.0% physiochemical entered/total 8/29 = 27.6% 13/30 = 43.3% nutrient entered/total 12/29 = 41.4% 11/30 = 36.7% Statistics SOC times/ |average| 8/0.44 6/0.30 pH times/ |average| 4/0.37 6/0.48 EC times/ |average| 0/0 2/0.17 MAT times/ |average| 6/0.30 4/0.35

Table 3. Stepwise regressions between glomalin features and various soil properties, and their diferences at diferent soil layers (Stepwise regression: F-enter probability value was at p < 0.01, and F-removal probability value was at p > 0.05). Te constants in the models were not listed in the table for minimizing the size of the table. Note: water is soil water percentage. SOC times mean the times of SOC entering the stepwise models, pH, EC and MAT times had the similar meaning. EEG/SOC, relative ratio of the carbon in EEG to total SOC; TG/ SOC, relative ratio of the carbon in TG to total SOC; EEG/N, relative ratio of the nitrogen in EEG to total soil N; TG/N, relative ratio of the nitrogen in TG to total soil N; EEG/P, relative ratio of the phosphorus in EEG to total soil P; TG/P, relative ratio of the phosphorus in TG to total soil P; EEG/TG, relative ratio of the EEG to TG.

soils were 0.44 and 0.37, and in the deep soils they were 0.30 and 0.48, showing an opposite tendency between the surface and deep soils in their regulation of glomalin-related features (Table 3).

Pearson correlation diference between the surface and the deep soils. Pearson correlations were also used to check the diferent associations between glomalin and various factors in the deep soils comparison with the surface soils (Table 4). In the case of soil physiochemical property-glomalin correlations, the coefcient

Scientific RepoRts | 7: 13003 | DOI:10.1038/s41598-017-12731-7 4 www.nature.com/scientificreports/

Parameter depth EEG TG EEG/SOC TG/SOC EEG/N TG/N EEG/P TG/P EEG/TG Soil physicochemical properties, stronger regulations at the deep soils than the surface soils Surface −0.100 −0.248** 0.313** 0.105 0.356** 0.160 0.065 0.025 0.240** Bulkdensity Deep −0.012 −0.255** 0.364** 0.053 0.395** 0.092 0.123 0.070 0.269** D/S — 1.03 1.17 — 1.11 — — — 1.12 Surface −0.010 0.075 −0.224** −0.168* −0.446** −0.332** −0.010 0.025 −0.124 soilwater Deep 0.213** 0.192** −0.267** −0.330** −0.168* −0.200** 0.056 0.029 0.088 D/S 21.3 2.56 1.19 1.96 0.37 0.60 — — — Surface −0.396** −0.338** −0.147 −0.204* −0.169* −0.179* 0.060 0.064 0.110 pH Deep −0.644** −0.592** −0.315** −0.341** −0.344** −0.413** 0.017 0.026 0.044 D/S 1.63 1.75 2.14 1.67 2.04 2.31 — — — Surface −0.094 −0.011 −0.176* −0.130 −0.068 −0.043 −0.043 −0.035 −0.115 EC Deep −0.419** −0.315** −0.391** −0.329** −0.414** −0.357** −0.085 −0.085 −0.063 D/S 4.46 28.6 2.22 2.53 6.09 8.30 — — — Soil nutrients, generally lower regulations in the deep soils than the surface soils. Surface 0.427** 0.682** −0.517** −0.262** −0.230** 0.002 0.099 0.147 −0.421** SOC Deep 0.414** 0.659** −0.280** −0.185** −0.174* −0.046 −0.069 −0.028 −0.177** D/S 0.97 0.97 0.54 0.71 0.76 — — — 0.42 Surface 0.261** 0.432** −0.370** −0.209* −0.583** −0.456** −0.011 0.031 −0.267** N Deep 0.323** 0.577** −0.257** −0.101 −0.333** −0.228** −0.070 −0.024 −0.192** D/S 1.23 1.34 0.69 0.48 0.57 0.50 — — 0.72 Surface 0.247** 0.314** −0.263** −0.181* −0.220** −0.143 −0.021 0.001 −0.114 AN Deep 0.107 0.291** −0.104 0.056 −0.097 0.046 0.000 0.015 −0.088 D/S 0.43 0.93 0.38 0.31 0.44 — — — — Surface 0.366** 0.235** 0.175* 0.139 0.268** 0.200* −0.001 −0.021 0.031 AP Deep 0.333** 0.327** 0.053 0.147* 0.072 0.168* −0.070 −0.068 −0.095 D/S 0.91 1.39 0.30 1.06 0.27 0.84 — — — Surface 0.153 0.330** −0.186* −0.050 −0.232** −0.079 −0.34** −0.34** −0.208* P Deep 0.128 0.294** −0.133 −0.025 −0.131 −0.008 −0.24** −0.26** −0.159* D/S — 0.89 0.72 — 0.56 — 0.71 0.76 0.76 Surface 0.042 −0.057 0.130 −0.002 0.129 0.035 0.086 0.073 0.105 K Deep 0.118 0.058 0.268** 0.260** 0.306** 0.303** 0.004 −0.028 0.009 D/S — — 2.06 130 2.37 8.66 — — — Surface 0.178* 0.240** −0.090 −0.039 −0.114 −0.064 −0.036 −0.027 −0.105 AK Deep 0.104 0.106 −0.131 −0.156* −0.107 −0.128 0.036 0.030 0.008 D/S 0.58 0.44 — 4.00 — — — — — Climatic conditions, at least similar infuences in the surface and deep soils Surface −0.349** −0.039 −0.297** 0.068 −0.276** −0.026 −0.141 −0.080 −0.353** MAT Deep −0.444** 0.054 −0.414** 0.114 −0.477** −0.008 −0.131 −0.046 −0.371** D/S 1.27 — 1.39 — 1.73 — — — 1.05 Surface 0.123 0.500** −0.314** 0.133 −0.348** 0.023 −0.148 −0.065 −0.451** MAP Deep 0.136* 0.526** −0.345** 0.109 −0.274** 0.176* −0.116 −0.059 −0.313** D/S 1.11 1.05 1.10 — 0.79 7.65 — — 0.69 Surface −0.031 0.256** −0.201* 0.129 −0.185* 0.046 −0.125 −0.082 −0.337** Altitude Deep 0.051 0.429** −0.198** 0.157* −0.165* 0.185** −0.081 −0.045 −0.236** D/S — 1.67 0.99 1.22 0.89 4.02 — — 0.70

Table 4. Pearson correlations between glomalin features and soil physiochemical properties, nutrients and climatic conditions, and diferences between the surface and deep soils. Note: Te same to Table 1. ** means that the coefcient was signifcant at p < 0.01, while * means that the coefcient was signifcant at p < 0.05. D/S means the ratio between deep soil to surface soil in the correlation coefcient.

ratio for the deep soils/surface soils for bulk density, soil pH and EC were generally higher than 1.0, showing larger coefcients were usually found in the deep soils (Table 4). In the case of nutrient-glomalin correlations, diferent patterns were found, i.e, general lower coefcients at the deep soils were observed compared with those in the surface soils. For example, SOC-glomalin coefcients, AN-glomalin coefcients, and P-glomalin coefcients in the deep soils were lower than those in the surface soils. While most of N, AP and AK showed the similar tendency, except K showed a contrary tendency (Table 4). Climatic-glomalin correlations showed general similar coefcients between the surface and deep soils, while diferent climatic parameters showed diferent patterns. For example, MAT-glomalin coefcients in the deep soils

Scientific RepoRts | 7: 13003 | DOI:10.1038/s41598-017-12731-7 5 www.nature.com/scientificreports/

Figure 1. RDA ordination-based glomalin variation partitioning at the surface soils (a) and the deep soils (b). In the case of the surface soil, soil nutrients are responsible for more glomalin variation (44.9% for the unique efect; 72.7% for the pooled efect) than in the deep soils (29.3% for the unique efect; 50.6% for the pooled efect); However, physiochemical properties of soil are responsible more variation in the deep soils (17.1% for the unique efect; 39.9% for the pooled efect) compared with the surface soils (14.2% for the unique efect; 22.4% for the pooled efect). Climatic conditions showed important efects in regulating glomalin accumulation in both the surface (20.1% for the unique efect) and the deep (29.1% for the unique efect) soils.

were 1.05–1.73 fold higher than those in the surface soils. Four MAP-glomalin coefcients showed the higher values in the deep soils, while the other 3 coefcients showed the higher values in the surface soils (Table 4).

RDA variation partitioning and diference between the surface and deep soils. By using the total variations of glomalin parameters as a whole (100%), RDA variation partitioning results showed that in the surface soils, the unique efect from soil nutrients contributed 44.9% (c) of the glomalin variations, while the deep soils showed a lower percentage (c, 29.3%). In contrary, glomalin variations in the deep soils could be explained more by unique efects from climatic conditions (a, 29.1%) and soil physiochemical properties (b, 17.1%), than did in the surface soils (a, 20.1% and b, 14.2% respectively) (Fig. 1). In the surface soils, interactions of climatic conditions, soil physiochemical properties and soil nutrients (d+e+f+g) were responsible for 20.7% of the vari- ations, and in the deep soils, they contributed 24.5% of the variations (Fig. 1).

Scientific RepoRts | 7: 13003 | DOI:10.1038/s41598-017-12731-7 6 www.nature.com/scientificreports/

Figure 2. SEM analysis on the causal relations between soil depths, climate, soil nutrients and physiochemical properties and glomalin features. Te signifcant direct coefcients were shown in the fgures, while non- signifcant ones were not shown here. Te regression weights with a statistical signifcance p < 0.0001 were listed in the fgures. PCA1 and PCA2 are the frst and second principal component for climatic conditions(climatic- PCA), soil physiochemical properties (soilproperty-PCA) and soil nutrients(nutrient-PCA). EEG/SOC, relative ratio of the carbon in EEG to total SOC; TG/SOC, relative ratio of the carbon in TG to total SOC; EEG/N, relative ratio of the nitrogen in EEG to total soil N; TG/N, relative ratio of the nitrogen in TG to total soil N; EEG/P, relative ratio of the phosphorus in EEG to total soil P; TG/P, relative ratio of the phosphorus in TG to total soil P; EEG/TG, relative ratio of the EEG to TG.

Pooling the data together, we found the similar tendency in total efects. For example, at the surface soils, total explaining percentages from climatic conditions (a+d+g+f) and soil physiochemical properties (b+d+g+e) were 27.4% and 22.4%, respectively; However, the explaining percentages in the deep soils became larger (47.8% and 39.9%, respectively). In contrary, explaining percentage from soil nutrients (c+e+f+g) in the surface soils (72.7%) was much larger than did in the deep soils (50.6%) (Fig. 1).

SEM analysis on the causal relations for glomalin changes: direct and indirect effects. A PCA method was used to extract the main information from the data. In the climatic conditions, one main component was derived, and 73.6% of the variation was included in the main component (climatic-PCA1) (Table A1; Fig. 2). In climatic-PCA1, MAT, MAP and altitude showed the similar contribution as shown in the quite similar standard coefcient (0.36–0.41) (Fig. 2; Table A2). Soil physicochemical properties component analysis showed that 2 main component extracted 63.9% information from the raw data (soilproperty-PCA1, 35.7%; soilproperty-PCA2, 28.2%). Soilproperty-PCA1 included information from bulk density (0.51) and soil water (−0.56); while soilproperty-PCA2 mainly included information from EC (0.74) and pH (0.52) (Fig. 2; Table A2). Two main components from soil nutrients extracted total 57.6% information from the raw data. Of them, nutrient-PCA1 contributed 42.8% and nutrient-PCA2 contributed 14.9% of the variations (Table A1; Fig. 2). Nutrient-PCA1 extracted information mainly from SOC (0.30), N (0.29), AN (0.24) and TP (0.24); while nutrient-PCA2 extracted information mainly from K (0.72), AP (0.62) and AK (0.22) (Fig. 2; Table A2). As shown in Fig. 2 and Table A3, a direct efect of soil depth infuence on the glomalin features were only found in EEG (−0.26), while all other vertical diferences were from the indirect efects via soil physiochemi- cal properties and soil nutrients. For example, the deeper soils directly resulted in the higher soil bulk density but the lower soil water (soil propertyPCA1, 0.32), which fnally resulted in the lower EEG (−0.26), EEG/N (−0.63) but the higher TG/SOC (0.15), and the indirect coefcients for these infuences were respectively −0.08 (=0.32*−0.26), −0.20 (=0.32*−0.63) and 0.05 (=0.32*0.15). Te indirect efects might also be achieved from the soil nutrientPCA1 (mainly SOC and N) (Fig. 2), i.e., the deeper soils could directly result in a lower SOC and N (−0.70), and this nutrient shortage in the deep soils resulted in the lower glomalin (0.33 for EEG and 0.55 for TG), but the higher glomalin contribution to SOC (−0.44 for EEG/SOC, −0.63 for TG/SOC) and N (−0.52 for EEG/N, −0.74 for TG/N); Te corresponding indirect efects were −0.23 to −0.39 for glomalin (EEG and TG), 0.31 to 0.44 for glomalin/SOC, 0.36 to 0.52 for glomalin/N (Fig. 2). Climatic conditions at the sampling sites also directly regulated the EEG accumulation (−0.34), but no efects on the TG, showing that the higher MAT, MAP and altitude were in line with the lower EEG accumulation in soils. Climate could indirectly afect the glomalin features through its signifcant infuences on the soil proper- tyPCA1 (bulk density and soil water), the soil propertyPCA2 (EC and pH) and the soil nutrientPCA1 (SOC and N), but not through the nutrientPCA2 (K and AP) (Fig. 2). Te higher climate-PCA1 value directly resulted in the lower soilpropertyPCA1 (i.e., a higher soil water but a lower soil bulk density, coefcient −0.41), and the lower soilpropertyPCA2 (i.e., a lower EC and pH, coefcient −0.17), but the more fertile soils (i.e., a higher SOC and N, coefcient 0.27); Tese changes in soil physiochemical properties and nutrients resulted in glomalin changes directly (Fig. 2; Table A3).

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In all, SEM results manifested that the soil depth and the climatic condition could directly afect EEG accu- mulation, while its efects on glomalin contribution to SOC, N and TG indirectly occurred through their strong associations with soil physiochemical properties and soil nutrients related with SOC and N (Fig. 2; Table A3). Discussion Lower glomalin amounts but larger contribution to and nutrient storage in the deep soils. In this paper, we found that much lower glomalin accumulated in the deep soils, while their contribution to SOC and nutrients in the deep soils was 1.1–1.3-fold higher than did in the surface soils, showing the importance glomalin-related carbon and nutrients in the deep soils compare with those in the sur- face soils. Previous studies also found 3.1–3.3-fold higher glomalin in the surface soils, compared with the deep soils in farming-pastoral zones36. In a shallow 40 cm soil profle, soil glomalin exhibits obvious vertical distribu- tion pattern, which decreases with increasing soil depth, and soil glomalin is signifcantly directly related with soil available phosphorus and protease19. Te contribution of glomalin to dissolve organic carbon difered in land uses and seasonality in dry tropics, and the ratio of glomalin-C to dissolve organic carbon was lower in forests than fallow lands and agriculture lands37. Deep is a key but poorly understood component of ter- restrial C cycle, and generally, C in the deep soil horizons is characterized by high mean residence times of up to several thousand years38. GRSP composition was a rich mixture of proteinaceous, humic, lipid and inorganic sub- stances39, and is extremely stable in nature1,2. SOM in subsoils is enriched in microbial-derived C compounds and could be depleted in energy-rich plant material compared to topsoil SOM38. Consequently, the increased fraction of glomalin-C to SOC with increasing sampling depth might provide new evidence that microbial derived C, especially AMF-C, promoted SOC accumulation in the subsoil9. One of the most important factors leading to protection of SOM in subsoils may be the spatial separation of SOM, microorganisms and extracellular enzyme activity possibly related to the heterogeneity of C input38. Our fndings indicate that the higher contribution of glomalin to SOC in the deep soils may possibly contribute the stronger stability of SOC in the deep soils. GRSP is a glycoprotein with a major asparagine-linked (N-linked) chain of carbohydrates, tightly bound with iron together with organic matter and amino acids13, possibly an important proxy of AMF amounts in soils40. Deep soil carbon and nutrient depletion have been observed in forests in NE China34, and also in other difer- ent studies, such as Amazonian forests41, reforestation on grasslands42 and croplands43. Most of the glomalin compositional traits were stable with soil physiochemical changes14,44 showing that this kind of glomalin-related nutrients may possibly restrict the nutrient supply for subsoil microbial activities. Actually, the absence of fresh organic carbon, an essential source of energy for soil microbes, the stability of organic carbon in deep soil layers is maintained20. Te much higher nutrient allocation in glomalin is a future of the deep soil nutrients, and this is a supplement for previous studies.

Lower nutrient- but higher physiochemical-regulations on glomalin in the deep soils. How to identify the possible factors afecting GRSP changes still have challenges in a feld sampling campaign, and sta- tistical methods including regression analysis, redundancy ordination, PCA and SEM analysis were used in this paper, which has been proved benefcial for determining the causal relationship between patterns26. Accordingly, we confrmed that diferent glomalin-regulating mechanism between the surface soils and the deep soils. In the surface soils, glomalin was regulated more by soil nutrients (mainly SOC and N), while soil physiochemical properties (e.g. pH) more greatly regulated glomalin features in the deep soils. Variation partitioning from RDA ordinations showed 17.5% more glomalin variations could be explained by soil physiochemical properties at the deep soils, while 22.1% more variations could be explained by soil nutrients in the surface soils (Fig. 1). Te importance of soil depth in regulating glomalin regulations could be manifested by two aspects via the SEM anal- ysis. Te frst is its direct infuences on EEG accumulation; Te second is the strongly indirect efects through the depth-dependent soil nutrients and physiochemical changes (Fig. 2). Te general tendency is that the higher soil nutrients, the lower values in soil bulk density, soil pH and EC accompanied with the higher glomalin accumula- tion in soils, but lower glomalin contribution to the SOC and N storage. With soil depth deepened, less EEG could be accumulated, but much slight infuences on TG. Deep soil diferences from the surface soils have been ofen reported in previous studies. Occlusion within soil aggregates has been identifed to account for a great proportion of SOM preserved in subsoils38. Deep soil horizons are recognized great contribution and importance to soil carbon pools, thus are also important in assess- ing whole- response to management and global change18, possibly through the close relation between SOC vertical distribution and local climate45 or deep soil water in modulating climate46. Te surface-deep soil diferences in factors regulating glomalin distribution, i.e., lower nutrient (SOC)- but higher physicochemical (pH)-regulations on glomalin in the deep soils were found when compared with the surface soils, provided a new evidence of the deep soil organic matter importance for terrestrial C and nutrient cycle from the view point of AMF and glomalin contributions34,38. Te soil conditioning function of GRSP has well reported, and under- standing of how to improve GRSP accumulation in soil is important for the GRSP-oriented degraded soil reha- bilitation14,44. Long-term land uses could afect fractions of glomalin and SOC in the Indo-Gangetic plain10. Te primary forests had 2.35–2.56-fold higher GRSP amount than those in the plantation forests and farmlands14, while the GRSP amount signifcantly correlated with soil bulk density and soil water47. However, from land uses changes were not well-defned to date. Our paper gave a supplement on this data shortage on the underlying forming mechanism for glomalin changes.

Similar climatic regulations on glomalin in the surface and deep soils. Te northeastern China is a sensitive region of climate change, and potential evapotranspiration (PE) and moisture index (MI) based on the observation data of 1961–2004 from 94 meteorological stations showed a general increasing tendency in MAT, MAP, PE and MI48. Moreover, an increasing tendency was more signifcant in MAT and PE than in MAP and

Scientific RepoRts | 7: 13003 | DOI:10.1038/s41598-017-12731-7 8 www.nature.com/scientificreports/

MI48, showing that northeast China is the most serious region processing in global changes, especially tempera- ture warming. Although the carbon sink capacity of the forest in Northeast China has been weakened since 2003, the total carbon absorption will keep increasing, and will play a positive role in the mitigation of climate change from the signifcant carbon sink capacity49. Underground soil changes during the global warming process were still in controversial, particularly the AMF and glomalin-related processes, although a previous study has found that GRSP could response to elevated CO2 and N addition in a subtropical forest, which favors the potential consequences for soil C accumulation9. In this paper, we found that climatic warming could possibly decrease the glomalin accumulation in soils, EEG/TG ratio and glomalin contribution to SOC and nutrients (Table 3 and Table 4). Moreover, the SEM analysis showed that EEG was directly down-regulated by climatic changes (warming, precipitation and altitude), while the others were indirectly afected from the climatic infuencing on soil properties and soil nutrients (Fig. 2). Both stepwise regression statistics, Pearson correlation and RDA-variation partitioning clearly manifested that the climatic change infuences on glomalin in the deep soils were similar to those in the surface soils. Moreover, the interactions between climatic changes, soil physiochemical properties and soil nutrients also largely regulating the glomalin accumulation both in the surface and deep soils. Until recently, the properties and dynamics of C and nutrients in the deep soils were largely ignored38. Forest growth could result in SOC accumulation, taking a large percentage of whole forest ecosystem carbon sequestration34,50; Moreover, deep soils showed a contrary tendency than did in the surface soils in nutrient dynamics34,50. Our fndings indicate that the climatic infuences on glomalin accumulation can reach as deep as 100 cm in soil depth, and possible underlying reason should be related with soil-mycorrhizal interactions. ’s efects on soils were possible from particle size, surface structure, mineral crystallinity, functional groups, and elemental composition of soil colloids and enzyme inter- actions51–53. Microbial priming efects are possibly related to the vertical patterns since the supply of new carbon into subsoils could stimulate the microbial mineralization of 2,567-year-old carbon20. In future studies, glomalin changes in the deep soils together with AMF diversity should be focused in studying soil processes in climatic changes.

Glomalin importance both in the surface and deep soils: implications from this study. Te United Nations has completed the frst-ever global assessment of the state of the planet’s land resources in late 2011, fnding that a quarter of all farmland is highly degraded and warning the trend must be reversed if the world’s growing population is to be fed (http://www.theage.com.au/). In China, the grain production in Songnen Plain accounts for over 10% of total commercial grain output in China54, owing to fertile black soils in this region55,56. However, soil degradation restricts agricultural development57,58 as shown be a 50% reduction in SOM and soil fertility59, serious saline-alkalinization60 and soil physical degradation of soil bulk density increase and decrease of total porosity50. Te following suggestions for northeastern China farmland development could be proposed based on the results of this study. Firstly, the underlying mechanism in regulating glomalin accumulation in the surface soils provide a basis for degraded soil improvement, and soil organic amendments and acidity adjustments should be possible meas- ures owing to its strongly regulating on glomalin accumulation. Previous studies have manifested that the SOC decreases and bulk density increases closely associated with the declines in GRSP amounts and compositional traits14,40,44,58, and some active steps should be supplemented for improving GRSP accumulation for securing their soil conditioning function14,61. As shown in this study, SOC is the top important regulator for glomalin accumula- tion in the surface soils, thus, future measures to improve glomalin in farming soils should consider more organic material returning to the soils. For example, organic manure amendments should be highlighted, and some stud- ies also have shown that redistribution of glomalin in macroaggregates possibly contributed to soil aggregates sta- bility62. Long-term organic fertilization practices could alter soil ergosterol content, glomalin, and phospholipid fatty acid profle63. By using glomalin as the activity of AMF, over 20-yr survey showed that glomalin increased under no-till and organic management owing to changes in AMF communities64. Organic, unlike conventional practices, promoted AM colonization in apple, and possibly associated with soil pH, P, Zn, Mn, C and leaf P, Ca, Mg and tree growth 65. Furthermore, soil pH was also very important for the glomalin accumulation. At present, a total of 3.73 million hm2 saline-sodic lands distributes in the Songnen Plain and saline-alkalization becomes more serious under global warming process in this region66. Anti-measures to decline the processes of saline-alkalization will beneft the accumulation of glomalin in soils, such as anti-agent additions60 or suitable species selections for acidifying soils67. Secondly, the much stronger regulations on the deep soil glomalin suggested that future evaluation of gloma- lin and AMF importance should fully consider deep soils. New mechanical understanding of AMF and soil prop- erties relations have been proposed, such as Clemmensen, et al.68’s mathematical partitions of fungi-C from soil C, as well as Cardoso and Kuyper69’s -assistance for plant P transporters with indirect efects on N availa- bility. Deep soil importance has been highlighted by the review paper38 and some research paper34. In this paper, we confrmed that regulating mechanism of glomalin accumulation difered from the surface and deep soils, and they should be related with the climatic, soil physiochemical and nutrient adjustment. In the future assessment of glomalin importance should include both the surface and deep soils together for an exact evaluation. Conclusion GRSP showed obvious vertical changes in both its amounts, glomalin/SOC, glomalin/nutrients, EEG/TG, and a lower glomalin amounts but a larger contribution to carbon sequestration and nutrient storage in the deep soils were generally observed. Soil physicochemical properties, soil nutrients and local climates closely associated with these vertical glomalin variations. Te diferent statistical analysis confrmed that lower nutrient- but higher physiochemical-regulations on glomalin features were in the deep soils compared with those in the surface soils (mainly SOC and pH-related regulation), while the similar climatic regulations on glomalin were found on the

Scientific RepoRts | 7: 13003 | DOI:10.1038/s41598-017-12731-7 9 www.nature.com/scientificreports/

Figure 3. Distribution of sampling locations across the Songnen Plain in northeastern China and their basic information. Te map was generated by sofware ArcGIS 10.0 (www.arcgis.com/) by Zhong Zhaoliang.

surface and deep soils. Our results highlight the importance of soil fertility, SOC and pH in regulating glomalin characteristics, and these regulations were depth-dependent. Tese fndings provide a basis for GRSP-oriented degraded soil recovery in black soil region in northeastern China, as well as scientifc evaluations of glomalin and AMF importance in whole soil profle into 1 m depth. Materials and Methods Study areas and soil sampling. Soil samples were collected from 6 diferent locations randomly selected in farmlands at Songnen Plain, northeastern China. Te 6 locations are Du-Meng (mean annual temperature [MAT] 3.1 °C; mean annual precipitation [MAP] 421 mm; altitude 147 m), Lan-Ling (MAT 4.4 °C; MAP 481 mm; altitude 381 m), Ming-Shui (MAT 2.9 °C; MAP 480 mm; altitude 259 m), Zhao-Dong (MAT 3.8 °C; MAP 467 mm; altitude 166 m), Zhao-Zhou (MAT 3.5 °C; MAP 450 mm; altitude 147 m), and Fu-Yu (MAT 3.0 °C; MAP 441 mm; altitude 158 m) (Fig. 3). At the sampling time, the crop at the sampling sites was maize, and previous crops were maize and soybean for many years. According to the Chinese Soil Classifcation System, the soil types in the study region are typical black soils, including Chernozem, Phaeozem, and Cambisols, and some degraded soil, such as Solonetz. According to the USDA Classifcation System, these belong to Mollisols, Entisols and Aridisols. In Dumeng, soil texture was sand (73%), silt (17%) and clay (10%); In Fu-Yu, sand was 42%, silt and clay were 34% and 24%, respectively; In Lan-Ling and Ming-Shui, sand were 19%, silt and clay were 55–58% and 22–25%, respectively. In Zhao-Dong and Zhao-Zhou, sand was 32–36%, silt was 47–51% and clay was 12–20%, respectively. In each of the six locations, 12 individual felds were chosen, and one soil profle in each feld plot was dug out for the soil sampling with a 100-cm3 cutting ring. In total, 360 soil samples (6 locations × 12 feld plots × 5 soil depths in the 1 m profle) were collected and placed in cloth bags, where they were air-dried until the weight was nearly constant. Te 5 soil depths were respectively 0–20 cm, 20–40 cm, 40–60 cm, 60–80 cm and 80–100 cm. All and gravel were carefully removed, the soil samples were ground, and passed through a 0.25-mm sieve prior to laboratory analysis50.

Extraction and determination of GRSP amount (EEG and TG). Extraction and determination of GRSP amount in soil were conducted as described by Wright and Upadhyaya70. EEG was extracted from 0.5 g soil with 4 mL of 20 mmol L−1 citrates (pH 7.0) and autoclaved at 121 °C for 30 min. TG was extracted from 0.1 g soil with 4 mL of 50 mmol L−1 citrates (pH 8.0) and autoclaved at 121 °C for 1 h. In both cases, the solution products were separated by centrifugation at 6000 g for 6 min to collect the supernatant. For TG, the described procedure was repeated several times, combining all extract solution from the same soil sample, until the reddish-brown color, which is typical of GRSP disappeared from the supernatant. Protein amount in the crude extract was deter- mined by the Bradford assay using bovine serum albumin as a standard14,70.

Determination of soil properties. The SOC was measured by the Tinsley method (heated dichro- mate/ titration method). The total-N was determined by the Semimicro-Kjeldahl method. The amount of alkaline-hydrolyzable N (AN) was determined by the alkaline hydrolyzed difusion method. Total P and K amounts were measured by the NaOH melt method. Te available P (AP) was extracted with 0.05 N HCl–0.025 N H2SO4 solution and reacted with a solution of l-ascorbic acid and H2SO4–(NH4)6Mo7O24 methods. Te available K (AK) was determined using fame photometry. All the above-mentioned methods were adapted from Bao71 and Wang, et al.50.

Scientific RepoRts | 7: 13003 | DOI:10.1038/s41598-017-12731-7 10 www.nature.com/scientificreports/

Te pH of soil solution (1 g soil in 5 mL deionized water) was measured using a pH-meter (Sartorius PT-21; Shanghai, China). The same soil solution was used for determining soil EC using an EC meter (DDS-307; Shanghai Precision Scientifc Instruments Co., Ltd., China). Both pH and EC measurement were determined at the same time. Soil gravimetric water was calculated as follows: [(Fresh weight − Air-dried weight) / Dry weight] × 100%. Soil bulk density was calculated as follows: Air-dried soil mass/soil volume of 400 cm3 50.

Data statistical analyses. Te Multivariate Analysis of Variance (MANOVA) with Duncan pairwise com- parison was used to compare the vertical variations in the glomalin features, soil physiochemical properties, and soil nutrient parameters. Pearson correlation, stepwise regression, Redundancy analysis (RDA) and structural equation model (SEM) analysis were used to uncover the associations between glomalin features and various factors of soil properties and climatic conditions, and possible diferences between the surface and deep soils. In the stepwise regression, criterion for the entering of a parameter is that inclusion at p < 0.01 and exclusion at p > 0.05; All the entered parameters for all the tested glomalin features were grouped as climatic conditions, soil physiochemical properties, and soil nutrients; Te more parameters entered the stepwise models, the more important of these factors for regulating glomalin features. Te RDA-related variation partitioning was used to separate the explaining percentage into climatic condi- tions, soil physiochemical properties, soil nutrients and their interactions. Te RDA ordination and variation partitioning were performed by Canoco 5.0 (Biometrics, Plant Research International, the Netherland). SEM, as a supplement of traditional statistical analysis of linear regression and stepwise regression, allows for the specifcation of system-level network hypotheses. To clarify, traditional statistical models are of the form y = f (X), where y is some response variable of interest and X is a vector of predictor variables. However, this equational form provides no means for representing hypotheses about why x variables might be correlated. In contrast, SEMs are of the form Y = f (X, Y), which allows for the specifcation of network hypotheses in which each var- iable is seen to be part of a system of variables. As a result, we may test the idea that variable C is infuenced by the variable A through the mediating efect of B (i.e., A → B → C). Tis fexibility in equational representation has numerous benefts, including the representation of complete hypotheses and the discovery of unanticipated relationships (e.g., efects of A on C not through B)72. In this paper, SEM was used in identifying the direct and indirect efects from soil physiochemical properties, soil nutrient parameters and climatic conditions on glomalin features (EEG, TG, EEG/TG, glomalin/SOC, glomalin/N, glomalin/P) in the surface soils (0–40 cm) and deep soils (40–100 cm) separately. Te diferences between the surface and deep soils were used to describe casual rela- tions for the glomalin vertical pattern with soil properties and climatic diferences. Too many independent factors will hinder the explaining of independent factors on dependent glomalin features, thus the PCA method was used to extract the main information from the data of climatic conditions, soil physiochemical properties and nutrient parameters for simplifying the complex association in the SEM analysis. Te criterion for principal component selections is that eigenvalue larger than 1.0. Te analysis was performed by IBM SPSS AMOS 22.0 (IBM, Armonk, NY) for the SEM analysis and SPSS 22.0 (IBM, Armonk, NY) for the PCA extraction. In all the analysis, glomalin features were characterized as TG, EEG, EEG/TG (relative ratio of the EEG to TG), TG/SOC (relative ratio of the carbon in TG to total SOC), EEG/SOC(relative ratio of the carbon in EEG to total SOC), TG/N (relative ratio of the nitrogen in TG to total soil N), EEG/N (relative ratio of the nitrogen in EEG to total soil N), TG/P (relative ratio of the phosphorus in TG to total soil P) and EEG/P (relative ratio of the phosphorus in EEG to total soil P). Te C (36.8%), N (3.7%), P (0.5%) and K (2.9%) content in extracted glomalin were cited from previous publication14 and used to compute the C, N, P and K amounts in EEG and TG. And these data were used in calculating glomalin contribution to SOC (EEG/SOC, TG/SOC), N (EEG/N, TG/N), and P (EEG/P, TG/P). Te glomalin contribution to K is 0.26% for TG and 0.03% for EEG, and they are ignored in the analysis owing to their neglectable amount. For fnding the diferences between the surface and deep soils, the association analysis (Pearson correlation, stepwise regression, RDA and SEM) were separately analyzed in 0–40 cm soils (surface) and 40–100 cm soils (deep).

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Acknowledgements Tis study was supported fnancially by the National Science and Technology Ministry (2016YFA0602304– 2), the Key Project (41730641) and the regular projects (41373075,31670699) from National Foundation of Natural Sciences of China, and the Fundamental Research Funds for the Central Universities (2572016AA12, 2572017DG04,DL13EA03-03). Author Contributions Wang Wenjie, Fu Yujie, He Xingyuan, and Wang Huimei conceived and designed the experiments. Wang Wenjie, and Wang Huimei contributed reagents, materials and analysis tools. Zhong Zhaoliang and Wang Qiong performed the experiments. Wang Wenjie, and Zhong Zhaoliang analyzed the data. Wang Wenjie and Wang Huimei wrote the paper. All authors approved the submitted and fnal versions. Additional Information Supplementary information accompanies this paper at https://doi.org/10.1038/s41598-017-12731-7. Competing Interests: Te authors declare that they have no competing interests. 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